Competence Center Emerging Technologies, Fraunhofer Institute for Systems and Innovation Research ISI, Breslauer Str. 48, 76139, Karlsruhe, Germany.
Institute of Ethics, History and Philosophy of Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
BMC Med Ethics. 2021 Aug 19;22(1):112. doi: 10.1186/s12910-021-00679-3.
Machine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians' practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians' competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician-patient relationship might be affected by the employment of ML_CDSS.
Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient's concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.
Using ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient's personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient's concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.
基于机器学习的临床决策支持系统（ML_CDSS）越来越多地应用于医疗保健的各个领域，旨在通过将个体患者的特征与计算机化的临床知识库相匹配来支持临床医生的实践。一些研究甚至表明，ML_CDSS 可能在特定孤立任务方面超越医生的能力。然而，从伦理角度来看，ML_CDSS 在医疗实践中的使用涉及一系列基本的规范性问题。本文旨在通过使用专业化理论作为分析视角，探讨 ML_CDSS 在医疗实践中的使用如何影响微观层面的医疗行为和医患关系，从而为伦理讨论增添内容。
专业化理论作为一种独特的社会学框架，提供了对构成客户相关专业行为（如医疗行为）的核心内容的详细说明，以及为什么它不仅仅是基于专业知识的行为。通过介绍专业化医疗实践的五个一般结构特征来引入专业化理论：（i）患者有关注点；（ii）医生处理患者的关注点；（iii）医生在不施惠的情况下提供帮助；（iv）医生以整体方式看待患者，而不建立私人关系；（v）医生将其一般专业知识应用于个体案例的特殊性。然后，对使用 ML_CDSS 的这五个关键方面进行分析，从而将专业化理论和医学伦理的观点结合起来。
在医疗实践中使用 ML_CDSS 需要医生特别关注那些不能被 ML_CDSS 全面考虑的个体案例事实，例如患者的个性、生活状况或文化背景。此外，ML_CDSS 在临床实践中的使用越常规化，医生就越需要关注患者的关注点并增强患者的自主性，例如，通过充分将数字决策支持整合到共同决策中。
